About Geotab

Canada-based Geotab provides open platform fleet management solutions for businesses of all sizes. Geotab’s software leverages real-time and historical trip data from more than 1 million in-vehicle sensors to help businesses of all sizes more efficiently manage drivers and vehicles. Founded in 2000, Geotab is one of the world’s fastest-growing telematics company.

About SpringML

SpringML is a Google Cloud Platform premier partner with specialization in Machine Learning and Data Analytics. The company’s mission is to help customers accelerate time to derive insight and make an impact using data.

Geotab is in the business of providing data-driven insights on commercial fleet vehicles on every continent. From engine speeds to ambient air temperatures to driving and weather conditions, Geotab can record a wealth of data through the Geotab GO telematics device and range of integrated sensors and apps.

The Canada-based company’s software platform, MyGeotab, enables its customers to gain insights from of all that raw vehicle data. For example, global manufacturers and distributors use MyGeotab to identify inefficiencies from data related to driving patterns, which helps them optimize deliveries, drivers, and vehicles.

With its devices in vehicles ranging from lawn mowers to heavy duty trucks, Geotab’s business is booming. Over the past few years, the company has doubled its workforce from 200 to over 400 and more than doubled the number of vehicles from which it captures data, from 400,000 in 2016 to nearly 1.2 million today. Geotab is one of the world’s fastest growing telematics companies.

Because deriving insight from data is Geotab's lifeblood, the company needs a fast, reliable, highly secure, and scalable database infrastructure. Previously, Geotab self-hosted all customer database servers on premises. “Managing physical server and network infrastructure got to be too complex, and when you're trying to aggregate data across multiple servers, it gets even more complex,” says Mike Branch, Vice President of Data and Analytics for Geotab.

“We stream about 2.5 billion raw data records every day into Google BigQuery. The speed with which we can access that data is second to none. It takes only 5 to 10 seconds from the time data is collected from one of our sensors until it’s available for analysis in Google BigQuery.”

—Mike Branch, VP of Data & Analytics, Geotab

To address those challenges, the company chose to migrate all of its server infrastructure to Google Cloud Platform (GCP). Currently, the MyGeotab platform is hosted across over 500 virtual machines in Google Compute Engine and is growing at a rate of 3 to 4 servers every week.

Google BigQuery is also particularly key for Geotab. In addition to leveraging the GCP Compute infrastructure for its MyGeotab platform, the company also pushes data into a unified data lake in Google BigQuery to take advantage of its high-speed streaming insertion API, reliability, scalability, support for standard SQL, and simplified management.

From sensors to analysis in seconds

“We stream about 2.5 billion raw data records every day into Google BigQuery, and we have more than 1 petabyte of data stored in Google BigQuery,” says Mike. “The speed with which we can access that volume of data is second to none. It takes only about 5 to 10 seconds from the time data is collected from one of our sensors until it’s available for analysis in Google BigQuery, which is great, especially for our fleet customers who depend on real-time data feeds from us.”

The high availability of Google BigQuery is another big advantage for Geotab. “We run our business off Google BigQuery, so we appreciate that it’s very reliable,” says Mike. “Reliability is also important to our customers, because they’re running their businesses with the help of Geotab data insights. If our system went down for any length of time, it could be seriously detrimental to customers that rely on the second-to-second nature of the data and may need to quickly respond to an emergency.”

One of the biggest Google BigQuery benefits to Geotab is that it supports a standard SQL syntax, which helps reduce the need to rewrite code—or train new hires on how to use Google BigQuery. “When you onboard a new hire, you want them to be productive as quickly as possible,” says Mike. “I can take someone who knows traditional SQL and have them start working right away in Google BigQuery, with no downtime. It’s phenomenal.”

Creating a competitive edge

GCP managed services such as Google BigQuery, free engineers to focus on new feature and software development instead of infrastructure maintenance and administration. “We don’t want to manage a complex infrastructure,” Mike says. “That’s not our focus. Because GCP removes so much of the management burden, we can focus on what we do best and create more value for our customers.”

Converting raw sensor data into insights specific to its fleet customers is one of Geotab’s competitive differentiators. For example, many of its competitors can only benchmark their data based on the industry their customer is in or the type of vehicles they’re operating. But Geotab is able to benchmark its data based on more granular, contextual factors, such as the movement patterns of different vehicles in a mixed-fleet environment.

“With standard SQL syntax in Google BigQuery, we can aggregate all data together in a single database and data warehouse to develop specific benchmarks for them while still maintaining customer privacy,” Mike says. Geotab also leverages TensorFlow, an open source machine learning framework that Google developed, to apply machine learning models to the raw data in order to deliver context-specific benchmarks for customers.

“We can classify about 20 different vehicle driving patterns,” Mike says. “We can understand the differences between how someone drives a van for a pest control company and how someone drives a truck for an oil company, which helps our customers make more informed optimization decisions using real-world data. Without Google BigQuery and TensorFlow, we wouldn’t have the ability to benchmark data for customers like this at scale, which is a key differentiator for us.”

“The Google Professional Services team came in to train us for a week. They focused on how to help us use machine learning to overcome the issues we outlined in our problem statement. Their post-training support has really helped us springboard into machine learning.”

—Mike Branch, VP of Data & Analytics, Geotab

Google Kubernetes Engine delivers the infrastructure agility and massive scale required by Geotab’s data ecosystem. For example, Geotab receives GPS records and engine data from its telematics devices and needs to contextualize much of the data at a high frequency. Leveraging Kubernetes pods has helped Geotab maintain a low latency of data ingestion into Google BigQuery.

“As we continue our rapid growth in the number of vehicles on the Geotab platform, we can simply optimize the number of pods required to maintain a low latency,” explains Mike. “This will allow us to provide localized insight at scale across multiple customers, such as which road segments are experiencing traction issues due to black ice.” Geotab can take a series of GPS records coming in and fire up a Kubernetes container, and quickly marry together the GPS coordinates and try to find out things like street names and waypoint IDs by pinging its map servers. As a result, the company can rapidly add more context to incoming GPS data.

Support from Google and SpringML

Google Professional Services team members played a key role in helping Geotab leverage machine learning. “We had some folks on our team who were really skilled in this area, and some who weren’t,” says Mike. “The Google Professional Services team came in to train us for a week. They focused on how to help us use machine learning to overcome the issues we outlined in our problem statement. Their post-training support has really helped us springboard into machine learning.”

In addition, Geotab worked with SpringML, a Google Cloud Premier Partner with a specialty in machine learning and data analytics. After the experts in the Google Professional Service team designed the approach for building statistical and machine learning models, SpringML helped to build the models. “SpringML helped us brainstorm how to leverage machine learning to offer greater value and insights to our customers,” Mike says. “Working with SpringML and Google led our team down the path towards creating features we use today in our machine learning algorithms to classify the location of a vehicle. This has ultimately proven to be a critical component of benchmarking and is in fact the number one predictor of engine idling in fleets.”

Crucial FedRAMP certification

GCP is a U.S. Federal Government FedRAMP-certified cloud service. “FedRAMP certification on our servers is crucial for us, especially for our government customers,” Mike says. “Without it, we’d need a more complex and costly infrastructure, with some operations running on Google servers and other operations that require FedRAMP certification running on another cloud provider’s servers.”

Expanding beyond fleet management

Smart city initiatives are also fueling Geotab’s momentum. Its devices can track road conditions, such as where potholes occur; locations where drivers have difficulty finding parking; even weak cell phone coverage areas. These rich data insights are enabling the company to expand beyond commercial fleet optimization with its new tool to help enable Smart Cities: data.geotab.com. The tool helps municipalities seeking to leverage data-driven insights to tackle urban transportation issues such as road congestion, hazardous intersections, and substandard road conditions.

With data.geotab.com, members of the public can tap into Google BigQuery to explore three different intelligence datasets: Location Analytics, Urban Infrastructure, and Weather, to see first-hand how to leverage the data analytics capabilities of Geotab.

“Google Cloud Platform is helping us transform the way we create value for our customers. We can contextually benchmark data at scale; develop richer, more extensive data insight; grow our smart city business; and share our intelligence data with the general public through our data.geotab.com site. Google Cloud Platform is helping us achieve all that, and more.”

—Mike Branch, VP of Data & Analytics, Geotab

“We have municipal users of our intelligence datasets that aren’t current fleet customers, and these datasets allow insight into the municipality beyond fleet operations, such as roads and works, transportation strategy, and infrastructure planning,” Mike explains. “Once a city learns the potential for these insights, it’s often spurred to outfit its own fleet with Geotab telematics to tap into even greater potential. We even see this level of insight becoming embedded in open data portals such as the Smart Columbus Operating System, where insights can be derived by marrying Geotab’s intelligence data together with municipal data. By crossing something as simple as the location of parking meters with Geotab’s Intelligence Dataset that highlights where people struggle to find parking spots, municipalities can optimize the placement of new parking meters, lessening traffic congestion, emissions, and driver frustration.”

Transforming the business

“Google Cloud Platform is helping us transform the way we create value for our customers,” says Mike. “We can contextually benchmark data at scale; develop richer, more extensive data insight; grow our smart city business; and share our intelligence data with the general public through our data.geotab.com site. Google Cloud Platform is helping us achieve all that, and more.”

About Geotab

Canada-based Geotab provides open platform fleet management solutions for businesses of all sizes. Geotab’s software leverages real-time and historical trip data from more than 1 million in-vehicle sensors to help businesses of all sizes more efficiently manage drivers and vehicles. Founded in 2000, Geotab is one of the world’s fastest-growing telematics company.

Industries:Technology

Location: Canada

About SpringML

SpringML is a Google Cloud Platform premier partner with specialization in Machine Learning and Data Analytics. The company’s mission is to help customers accelerate time to derive insight and make an impact using data.